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Pradeep Devaneyan, S.
- Cyberbully Detection from Twitter Using Classifiers
Authors
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, IN
2 School of Mechanical and Building Sciences, Christ College of Engineering and Technology, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 8 (2017), Pagination: 163-168Abstract
These days social communication networks become a part of the daily activity and the users of the social media also increased. The increasing use of social networks by their users leads to large amount of user communication data. And the popularity of social media causes cyberbullying and it became the major problem in communication through online. Cyberbullying leads to many severe problems, undesirable effect on human’s life and it also lead to suicides. In the existing system the unique information such as network, activity, user and tweet contents are extracted from Twitter. By the use of extracted information the cyberbullying words present in the tweet contents are detected using machine learning algorithms like Naïve Bayes, Random Forest, Support Vector Machine and KNN. In the proposed work the rumor tweets and cyberbully tweets are detected, along with these the cyberbully words in the tweet comments also detected using Random Forest and Naïve Bayes classifiers. The required information’s such as name, gender and age of the cyberbully tweeted persons are detected. By the use of twitter speech act classification features along with the machine learning classifiers, the rumor tweets are detected in this proposed work.
Keywords
Cyberbullying Detection, Data Preprocessing, Machine Learning Algorithms, Twitter, Feature Extraction, Rumor Detection.
References
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- Cyberbully Image and Text Detection using Convolutional Neural Networks
Authors
1 Pondicherry Engineering College., IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN
3 Christ College of Engineering and Technology, Puducherry, IN
Source
Fuzzy Systems, Vol 11, No 2 (2019), Pagination: 25-30Abstract
Social media is getting more and more popular in our day to day life. The popularity of social media affects the people involved in it. This makes the technology to do the work or to feel smarter and but only makes us lazy. Therefore, in this robust, discriminative and numerical representation, learning of text messages is a critical issue. Hence, the existing system helps to detect the cyberbully words using Naive Bayes Classifier. The output is classified into cyberbully and not cyberbully words from the Instagram dataset and accuracy is calculated. The proposed framework deployed for detecting negative online interactions in terms of abusive contents carried out through both text and images. This proposed technique is going to detect the cyberbully image and text on the Instagram dataset using Convolutional Neural Network and Bag of words techniques along with the existing technique. Thus, the detected cyberbully words are further classified using Naive Bayes classifier such as Harassing, Insulting, Trolling and Threatening. The combination of text & image analysis techniques is considered an appropriate platform for the detection of potential cyberbully threats.
Keywords
Bag of Words, Convolutional Neural Networks, Cyberbully Image, Cyberbully Words, Instagram Dataset, Naive Bayes Classifier.References
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